ArticlePsychometrika2020
Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data.
Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging Distance-Based Effectiveness Indicators for Enhanced Behavioral Pattern Discovery in Complex Problem-Solving Assessment.Behavioral sciences (Basel, Switzerland) · 2026Article
- Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis.Psychometrika · 2025Article
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2 authors.
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Abstract
Process data, which are temporally ordered sequences of categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful information. A process is a collection of time-stamped events of different types, recording how an individual behaves in a given time period. The process data are too complex in terms of size and irregularity for the classical psychometric models to be directly applicable and, consequently, new ways for modeling and analysis are desired. We introduce herein a latent theme dictionary model for processes that identifies co-occurrent event patterns and individuals with similar behavioral patterns. Theoretical properties are established under certain regularity conditions for the likelihood-based estimation and inference. A nonparametric Bayes algorithm using the Markov Chain Monte Carlo method is proposed for computation. Simulation studies show that the proposed approach performs well in a range of situations. The proposed method is applied to an item in the 2012 Programme for International Student Assessment with interpretable findings.
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